The association between vitamin D deficiency and multiple sclerosis: an updated systematic review and meta-analysis
Bibliographic record
Abstract
Background Although there is evidence of a link between vitamin D status and risk of multiple sclerosis (MS), there has been no systematic review where the exposure measure was vitamin D deficiency rather than 25 hydroxy vitamin D (25(OH)D) concentration. We conducted an updated systematic review and meta-analysis to estimate the association between vitamin D deficiency, defined in most studies as a serum 25(OH)D concentration of <50 nmol L -1 , and MS. Methods We searched the MEDLINE, EMBASE, and CINAHL databases to identify relevant publications. We estimated the pooled odds ratio (OR) using a random effects model for the association between vitamin D deficiency and MS, overall and stratified by several factors, including whether or not studies included participants who were taking vitamin D supplements. We also analysed the association between mean 25(OH)D concentration and MS, and used meta-regression to assess the effects of vitamin D supplementation, latitude, age, ethnicity, vitamin D definition and seasonality on the OR estimates. The Newcastle-Ottawa Scale was used to assess study quality. Results Results were pooled across 14 case-control studies published between 2007 and 2021 (n=4,130 cases, n=4,604 controls). Persons with vitamin D deficiency had a 54% higher risk of multiple sclerosis than those with sufficient vitamin D status (OR 1.54; 95% CI 1.05, 2.24). In studies that excluded participants taking vitamin D supplements (N=7), the OR was 2.19 (95% CI: 1.44, 3.35), whereas, in studies that did not exclude participants taking supplements, there was no increase in risk (OR 0.82; 95% CI: 0.43, 1.58). Mean age (R 2 =27.4%) and inclusion/exclusion of participants taking supplements (R 2 =33.4%) contributed most to variability in the OR of vitamin D deficiency and MS. Conclusion Vitamin D deficiency is associated with an increased likelihood of multiple sclerosis. Maintaining sufficient vitamin D may be an important modifiable risk factor for MS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.034 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".